Methods and systems for determining a quantity and a size distribution of products

ABSTRACT

A computer-implemented method may include obtaining article information associated with the one or more articles; obtaining historical transactional data associated with purchasing the one or more articles; obtaining, via the one or more processors, article preference data associated with purchasing the one or more articles; determining, via the one or more processors, one or more assumptions based on the article preference data; determining, via the one or more processors, the quantity associated with purchasing the one or more articles based on the article information and the one or more assumptions; determining, via the one or more processors, the size distribution associated with purchasing the one or more articles based on the determined quantity, the historical transactional data, and the one or more assumptions; and transmitting, to a purchaser, a notification indicating the quantity and the size distribution associated with purchasing the one or more articles.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toproviding information to a purchaser regarding purchasing one or morearticles, and, more particularly, to providing the purchaser with aquantity and a size distribution associated with purchasing one or morearticles.

BACKGROUND

Fashion and apparel style management may pose several challenges forapparel rental subscription services. For example, one such challengemay be that traditional processes of purchasing decisions for apparelrental subscription services may be error prone and labor intensive.Existing solutions may not be practical because existing tools orapplications for apparel rental subscription services may be built on astore-based model. Such store-based models for purchasing decisions maybe based on the number of stores that one or more articles will be soldin, and an anticipated average number that each store can sell in a weekor during a predetermined period of time (e.g., a lifetime of an articleor article category). In this situation, the lifetime of an article orarticle category may be considered to be between 12 and 26 weeks,depending on the article category. These existing solutions may notapply to apparel rental services, such as subscription-based servicesand/or clothing-as-a-service (CaaS), because purchasing decisions for arental service are not based on how much inventory will be sold, but onother factors. Additionally, rental inventory may last far longer thaninventory sold in stores, because an article that a customer wanted torent a year ago may still be relevant today.

A further challenge for rental services may be that many brands orclothing merchants for rental services may have different dates by whichpurchasing information (e.g., purchasing quantity) is due. Thus, it maybe difficult to have a full picture of what a seasonal buy (e.g., whatthe purchaser is purchasing) looks like across one or more attributes(e.g., what percentage of article categories are dresses, tops, orpants) until a final article category has been purchased, sincepurchases may be done on a rolling basis. In traditional retail, thischallenge may not occur since all styles in a given month or season maybe determined at the same time. Within traditional retail, purchasingquantities and size distributions may be determined by large teamsranging from purchasing, to merchandising, to planning, or toallocation. For an entity providing apparel rental subscription serviceswithout any physical stores (or in addition to physical stores), it maybe difficult to allocate the same resources as traditional retailers(e.g., having multiple employees to determine the purchasing quantitiesand size distributions).

Another difference between rental services and traditional retail may bethat when it comes to assortment planning, rental services may endeavorto create an optimal “portfolio” of articles that will appeal to a broadcustomer base. In traditional retail, however, the focus may be on howwell a given article category is likely to sell within given cost andpricing constraints. As an entity providing apparel rental subscriptionservices without any physical stores (or for subscription servicesprovided in addition to physical stores), it may be difficult to gainmore revenue for an article that has a relatively higher cost (e.g.,since revenue is generated from a monthly subscription fee and is nottied to the individual article that a given member is renting). Thus,the objective of the apparel rental and/or subscription services may beto purchase an assortment of articles that is appealing to a broadcustomer base of the apparel rental and/or subscription services whiletrying to meet financial and assortment targets of the entity.

The present disclosure is directed to overcoming one or more of theseabove-referenced challenges. The background description provided hereinis for the purpose of generally presenting the context of thedisclosure. Unless otherwise indicated herein, the materials describedin this section are not prior art to the claims in this application andare not admitted to be prior art, or suggestions of the prior art, byinclusion in this section.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods and systems aredisclosed for determining a quantity and a size distribution associatedwith purchasing one or more articles. The methods and systems disclosedherein may enable a single purchaser to independently assess thequantity and size distribution associated with purchasing the one ormore articles. The methods and systems disclosed herein may enable anentity providing apparel rental subscription services to purchase anassortment of articles that is appealing to a broad customer base whilemeeting all of the financial and assortment targets of the entity. Themethods and systems disclosed herein may ensure that when a last pieceof article in the assortment is purchased, seasonal inventory plans maybe achieved. The methods and systems disclosed herein may provideflexibility for changes that quickly and efficiently adjust one or moreassumptions based on shifts in business trends, financial plans, or thecustomer base. One or more assumptions may be adjusted automatically andapplied to new article categories that are purchasers without informingor interacting with one or more purchasers. It may be beneficial to havealignment with demand (e.g., how much a purchaser may purchase) andsupply (e.g., popularity of an article among customers or users), atboth the overall article category level (e.g., total quantity amongdifferent article categories) and size distribution. The method may helpavoid the situations with too much or too little inventory of articles.Too much inventory of articles may indicate money may have been spent onunnecessary articles, and too little inventory may mean customers orusers may not be able to rent the articles they want.

In an aspect, a computer-implemented method for determining a quantityand a size distribution associated with purchasing one or more articlesmay include: obtaining, via one or more processors, article informationassociated with the one or more articles, wherein the articleinformation comprises information of one or more attributes associatedwith the one or more articles, wherein the one or more attributesinclude at least one of an average unit cost, a type of customer, alevel of conviction, or an article category; obtaining, via the one ormore processors, historical transactional data associated withpurchasing the one or more articles, wherein the historicaltransactional data comprises at least a historical size distributionassociated with purchasing the one or more articles; obtaining, via theone or more processors, article preference data associated withpurchasing the one or more articles, wherein the article preference datacomprises at least one of season impact data or customer identificationdata; determining, via the one or more processors, one or moreassumptions based on the article preference data; determining, via theone or more processors, the quantity associated with purchasing the oneor more articles based on the article information and the one or moreassumptions; determining, via the one or more processors, the sizedistribution associated with purchasing the one or more articles basedon the determined quantity, the historical transactional data, and theone or more assumptions; and transmitting, to a purchaser, anotification indicating the quantity and the size distributionassociated with purchasing the one or more articles.

In another aspect, a computer system for determining a quantity and asize distribution associated with purchasing one or more articles mayinclude a memory storing instructions; and one or more processorsconfigured to execute the instructions to perform operations. Theoperations may include obtaining, via one or more processors, articleinformation associated with the one or more articles, wherein thearticle information comprises information of one or more attributesassociated with the one or more articles, wherein the one or moreattributes include at least one of an average unit cost, a type ofcustomer, a level of conviction, or an article category; obtaining, viathe one or more processors, historical transactional data associatedwith purchasing the one or more articles, wherein the historicaltransactional data comprises at least a historical size distributionassociated with purchasing the one or more articles; obtaining, via theone or more processors, article preference data associated withpurchasing the one or more articles, wherein the article preference datacomprises at least one of season impact data or customer identificationdata; determining, via the one or more processors, one or moreassumptions based on the article preference data; determining, via theone or more processors, the quantity associated with purchasing the oneor more articles based on the article information and the one or moreassumptions; determining, via the one or more processors, the sizedistribution associated with purchasing the one or more articles basedon the determined quantity, the historical transactional data, and theone or more assumptions; and transmitting, to a purchaser, anotification indicating the quantity and the size distributionassociated with purchasing the one or more articles.

In yet another aspect, a non-transitory computer readable medium for useon a computer system containing computer-executable programminginstructions for performing a method of determining a quantity and asize distribution associated with purchasing one or more articles. Themethod may include obtaining, via one or more processors, articleinformation associated with the one or more articles, wherein thearticle information comprises information of one or more attributesassociated with the one or more articles, wherein the one or moreattributes include at least one of an average unit cost, a type ofcustomer, a level of conviction, or an article category; obtaining, viathe one or more processors, historical transactional data associatedwith purchasing the one or more articles, wherein the historicaltransactional data comprises at least a historical size distributionassociated with purchasing the one or more articles; obtaining, via theone or more processors, article preference data associated withpurchasing the one or more articles, wherein the article preference datacomprises at least one of season impact data or customer identificationdata; determining, via the one or more processors, one or moreassumptions based on the article preference data; determining, via theone or more processors, the quantity associated with purchasing the oneor more articles based on the article information and the one or moreassumptions; determining, via the one or more processors, the sizedistribution associated with purchasing the one or more articles basedon the determined quantity, the historical transactional data, and theone or more assumptions; and transmitting, to a purchaser, anotification indicating the quantity and the size distributionassociated with purchasing the one or more articles.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts an exemplary environment in which methods, systems, andother aspects of the present disclosure may be implemented.

FIG. 2 depicts an exemplary flowchart illustrating a method fordetermining a quantity and a size distribution associated withpurchasing one or more articles, according to one or more embodiments.

FIG. 3 depicts an exemplary user interface presented on an employeedevice for a purchaser showing a quantity associated with purchasing oneor more articles, according to one or more embodiments of the presentdisclosure.

FIG. 4 depicts an exemplary user interface presented on an employeedevice for a purchaser showing a size distribution associated withpurchasing one or more articles, according to one or more embodiments ofthe present disclosure.

FIG. 5 depicts an exemplary chart illustrating average unit cost andquantity tolerance.

FIG. 6 depicts an exemplary chart illustrating average unit costpurchased for different price groups.

FIG. 7 illustrates an example of a computing device 700 of a computersystem.

DETAILED DESCRIPTION OF EMBODIMENTS

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection. Both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the features, as claimed.

In this disclosure, the term “based on” means “based at least in parton.” The singular forms “a,” “an,” and “the” include plural referentsunless the context dictates otherwise. The term “exemplary” is used inthe sense of “example” rather than “ideal.” The terms “comprises,”“comprising,” “includes,” “including,” or other variations thereof, areintended to cover a non-exclusive inclusion such that a process, method,or product that comprises a list of elements does not necessarilyinclude only those elements, but may include other elements notexpressly listed or inherent to such a process, method, article, orapparatus. Relative terms, such as, “substantially” and “generally,” areused to indicate a possible variation of ±10% of a stated or understoodvalue.

In the following description, embodiments will be described withreference to the accompanying drawings. As will be discussed in moredetail below, in various embodiments, data such as article information,historical transactional data, one or more assumptions, or articlepreference data may be used to generate a quantity and a sizedistribution associated with purchasing one or more articles.

The method described herein may determine article-category-levelquantity to reduce the amount of time a purchaser spends on determiningsuch quantity; allow the seasonal assortment to achieve topline quantityand average unit cost targets, as well as assortment targets (e.g.,overall seasonal targets set by the purchasers); enablearticle-category-level quantity to be roughly in line with units derivedfrom the existing or traditional processes. The method described hereinmay enable a purchaser or entity providing the apparel rentalsubscription services to build a spreadsheet-based or otherdatabase-based tool to calculate a quantity or size distributionassociated with purchasing one or more articles based on no more thanfive attributes. One or more factors may be used to determine whichattributes may be used and how values of the attributes can be changed.

FIG. 1 shows an exemplary environment 100, according to one or moreembodiments of the present disclosure. As shown, the exemplaryenvironment 100 may include one or more networks 101 that interconnect aserver system 102, user devices 112, employee devices 116 (e.g.,purchaser device), tenant devices 120, and external systems 122. The oneor more networks 101 may be, for example, one or more of a cellularnetwork, a public land mobile network, a local area network, a wide areanetwork, a metropolitan area network, a telephone network, a privatenetwork, an ad hoc network, an intranet, the Internet, a fiber opticbased network, a cloud computing network, etc. User devices 112 may beaccessed by users or customers 108, employee devices 116 may be accessedby authorized employees 114 (e.g., a purchaser), and tenant devices 120may be accessed by employees of tenant entities 118. In someimplementations, employee devices 116 may be used to perform thefunctions of the tenant devices 120 and/or the user devices 112. Serversystem 102 may comprise one or more servers 104 and one or moredatabases 106, which may be configured to store and/or process aplurality of data, microservices, and service components, and/orassociated functions thereof. In some embodiments, the server system 102may comprise an algorithm module. The one or more servers 104 maycomprise the algorithm module in some embodiments. The algorithm modulemay comprise a machine learning module including one or more neuralnetworks. In some embodiments, the one or more neural networks mayinclude deep convolutional neural networks (DCNN) and/or region basedconvolutional neural networks (R-CNN). R-CNNs may include one or moreconvolutional neural network models designed for object detection withinan image. DCNNs may be configured to analyze visual imagery, forexample, for analyzing, classifying, and identifying one or moreproducts within an image depicting the one or more products. In someembodiments, the one or more neural networks may comprise one or moreimage segmentation based neural networks and one or more imageclassification based neural networks.

Users or customers 108 may access the server system 102 through the oneor more networks 101 using user devices 112. Each device among the userdevices 112 may be any type of computing device (e.g., personalcomputing device, mobile computing devices, etc.) which allows users orcustomers 108 to display a web browser or a web based application foraccessing the server system 102 through the network 101. The userdevices 112 may, for example, be configured to display a web browser, aweb based application, or any other user interface (e.g., one or moremobile applications) for allowing users or customers 108 to exchangeinformation with other device(s) or system(s) in the environment 100over the one or more networks 101. For example, a device among the userdevices 110 may load an application with a graphical user interface(GUI), and the application may display on the GUI one or more apparelrecommendations for closeting by the user. Users or customers 108accessing user devices 112 may be, for example, users and/or potentialusers of apparel rental subscription services and/or apparel madeavailable for subscription based distribution via electronictransactions and physical shipment. Additionally, or alternatively,users or customers 108 may access user devices 112 to, for example,manage one or more user accounts, view catalogs, configure one or moreuser profiles, engage in customer service communications, make purchaseorders, track shipments, generate shipments, monitor order fulfillmentprocesses, initiate or process returns, order apparel for purchase,provide feedback, refer other users, navigate through various featuressuch as size advisor, perform personalized discovery, and/or makerecommendations.

Employee devices 116 may be configured to be accessed by one or moreemployees 114, including, for example, purchasers, customer serviceemployees, marketer employees, warehouse employees, analytics employees,or any other employees who are authorized and/or authenticated toperform tasks, operations, and/or transactions associated with theserver system 102, and/or the external systems 122. In one embodiment,employee devices 116 are owned and operated by the same entity or atleast an affiliate of the entity operating the apparel rentalsubscription services or e-commerce (e.g., CaaS) business hosted onserver systems 102. Each device among the employee devices 116 may beany type of computing device (e.g., personal computing devices, mobilecomputing devices, etc.). The employee devices 116 may allow employees114 to display a web browser or an application for accessing the serversystem 102 and/or the external systems 122, through the one or morenetworks 101. For example, a device among the one or more of theemployee devices 116 may load an application with a GUI, and theapplication may display on the GUI one or more warehouse operationsassociated with providing CaaS to users or customers 108. In someimplementations, the employee devices 116 may communicate directly withthe server system 102 via communications link 117 bypassing publicnetworks 101. Additionally, or alternatively, the employee devices 116may communicate with the server system 102 via network 101 (e.g., accessby web browsers or web based applications).

Tenant devices 120 may be configured to be accessed by one or moretenants 118. Each device among the tenant devices 120 may be any type ofcomputing device (e.g., personal computing device, mobile computingdevices, etc.). As used herein, each tenant, among one or more tenants118, may refer to an entity or merchant that allocates and/or suppliesone or more specific collections of apparel for the CaaS inventory. Forexample, each of the one or more tenants 118 may be a retailer, adesigner, a manufacturer, a merchandiser, or a brand owner entity thatsupplies one or more collections of wearable items to the CaaS inventorymanaged and/or accessed by the server system 102. Tenants 118 may useone or more electronic tenant interfaces (e.g., a catalog contentmanagement system associated with each tenant) to provide the serversystem 102 with wearable item data (e.g., apparel information) thatdescribe apparel or wearable items made available for electronictransactions on server system 102. For example, one or more catalogs foreach of the one or more tenants 118 may be generated and/or updated atthe server system 102 dynamically and/or periodically. Tenant devices120 may serve as access terminals for the tenants 118, for communicatingwith the electronic tenant interfaces and/or other subsystems hosted atthe server system 102. The tenant devices 120 may, for example, beconfigured to display a web browser, an application, or any other userinterface for allowing tenants 118 to load the electronic tenantinterfaces and/or exchange data with other device(s) or system(s) in theenvironment 100 over the one or more networks 101.

External systems 122 may be, for example, one or more third party and/orauxiliary systems that integrate and/or communicate with the serversystem 102 in performing various CaaS tasks. External systems 122 may bein communication with other device(s) or system(s) in the environment100 over the one or more networks 101. For example, external systems 122may communicate with the server system 102 via API (applicationprogramming interface) access over the one or more networks 101, andalso communicate with the employee devices 116 via web browser accessover the one or more networks 101.

As indicated above, FIG. 1 is provided merely as an example. Otherexamples that differ from the example environment 100 of FIG. 1 arecontemplated within the scope of the present embodiments. In addition,the number and arrangement of devices and networks shown in environment100 are provided as an example. In practice, there may be additionaldevices, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in environment 100. Furthermore, two or more devices shown in FIG.1 may be implemented within a single device, or a single device shown inFIG. 1 may be implemented as multiple, distributed devices.Additionally, or alternatively, one or more devices may perform one ormore functions of other devices in the example environment 100. Forexample, employee devices 116 may be configured to perform one or morefunctions of tenant devices 120, in addition to their own functions.

FIG. 2 is an exemplary flowchart illustrating a method for determining aquantity and a size distribution associated with purchasing one or morearticles. The method may be performed by server system 102.

Step 201 may include obtaining, via one or more processors, articleinformation associated with the one or more articles. The articleinformation may comprise information of one or more attributesassociated with the one or more articles. Such article information maybe provided by a purchaser via a user interface displayed on an employeedevice 116. The article information may be determined by one or morealgorithms (e.g., an algorithm that randomly defines articleinformation). The one or more attributes may include at least one of anaverage unit cost, a type of customer, a level of conviction, or anarticle category. The average unit cost may include the average cost ofone or more articles that a purchaser is willing to purchase during apredetermined period of time. The predetermined period of time may be atleast one day, one week, one month, one quarter, one year, or longer.The predetermined period of time may be at most one year, one quarter,one month, one week, one day, or less. The average unit cost may fallinto at least one or more price groups. The one or more price groups mayinclude a high price group, a middle price group, a low price group,and/or a minimum order quantity price group. One or more assumptionsand/or algorithms may be used to determine how the average unit cost ofone or more articles falls into one or more price groups. For example,an evening dress may fall into the high price group, a business suit mayfall into middle price group, and sportswear may fall into a low pricegroup. The minimum order quantity price group may be associated with anyarticles that belong to a minimum order quantity category. Details ofthe minimum order quantity category are described below. In one example,one or more assumptions may include an average unit cost of the highprice group equal to about $75.00, an average unit cost of the middleprice group equal to about $41.20, an average unit cost of the low pricegroup equal to about $22.00, and an average unit cost of the minimumorder quantity price group equal to about $40.50.

The one or more attributes may include a type of customer. The type ofcustomer may include at least one of a core customer or a nichecustomer. The core customer may represent a majority of the customers ofan appeal subscription service. The core customer may prefer the coreapparel or article category that are likely to have a broad appealacross the customers of the apparel subscription service. The corecustomer may show similar article preferences based on a trend orseasonal impact. The niche customer may represent a subgroup, but not amajority, of customers of an appeal subscription service. The nichecustomer may show different article preference from the core customerbased on a trend or seasonal impact. The niche customer may prefer nicheapparel or article categories that are likely to resonate with lessengaged customers of the apparel subscription services and may not be inline with preferences of the core customers. The one or more attributesmay include a level of conviction indicative of a prediction made by thepurchaser regarding renting the one or more articles to one or morecustomers. For example, predictions may show how strongly the purchaserfeels about one or more articles (e.g., based on the article categories)according to the purchaser's experience with one or more attributesassociated with the one or more articles. For instance, a purchaserhaving a high level of conviction on sportswear for college students ina future summer season may indicate that the purchaser has experiencewith high rental volume of sportswear for college students in the summerseason last year. The article categories may be divided based on anysuitable criteria. In one example, the article categories may includeblazer, coat, blouse, jacket, dress, jeans, jumper, pants, sweaters,swimsuit, T-shirt, shirt, suit, underwear, or gown. In another example,the article categories may include dress, pant, blazer, top, cardigan,skirt, or outwear.

The article information may further include data indicative of whetherthe one or more articles belong to a minimum order quantity category. Ifa merchant has a contractual relationship with the entity providing theapparel rental subscription service, the purchaser may purchase articlesfrom the merchant based on a predetermined quantity (e.g., a minimumquantity) for a certain article category. This article category maytherefore belong to a minimum order quantity category. In thissituation, the quantity of purchasing for the certain article categorymay be fixed, while the size distribution of purchasing the certainarticle category may be variant. To determine the size distribution,historical transactional data, such as historically predeterminedquantity for at certain article category, may be used.

As described herein, a merchant may be an entity that provides products.In this disclosure, the term “product,” in the context of productsoffered by a merchant, encompasses both goods and services, as well asproducts that are a combination of goods and services. In someembodiments, the product may include one or more articles.

The article information may further include data indicative of whetherthe one or more articles belong to a pre-pack category. If a merchanthas a contractual relationship with the entity providing the apparelsubscription service such that a purchaser cannot purchase a certainarticle category with flexibility in size distribution but can onlypurchase the certain article category with size distribution in apredetermined ratio, the certain article category may belong to apre-pack category. For example, the predetermined ratio may include aratio of size XS:size S:size M:size L equal to 1:1:1:1. A merchant whosells articles belonging to minimum order quantity category or pre-packcategory may not go through the whole method of determining a quantityand size distribution, since a quantity or size distribution may bedetermined based on a contractual relationship. For example, a merchantwho sells articles belonging to minimum order quantity category may havea fixed quantity determined by a contractual relationship, but the sizedistribution can be changed. In another example, a merchant who sellsarticles belonging to pre-pack category may have a fixed sizedistribution determined by a contractual relationship, but the quantity(e.g., number of packs) can be changed. Historical transactional data(e.g., historical quantity and size distribution) may be used todetermine the pre-determined quantity and/or size distribution ofarticles belonging to a minimum order quantity category or pre-packcategory.

The one or more attributes may include additional information relatingto the one or more articles, including, but not limited to, the brand ofthe one or more articles, the marketing strategy of promoting the one ormore articles, the cost of manufacturing the one or more articles,whether or not the one or more articles are associated with (e.g.,presented on) other apparel rental subscription services or otherentities providing the apparel rental subscription services, or thepacking method associated with the one or more articles. The number ofattributes used for the method disclosed herein may be at least 1, 2, 3,4, or more. The number of attributes may be at least 5, 4, 3, 2, orless. The number of attributes may be less than five. Since the numberof one or more attributes may have a maximum value, the criteria toselect the one or more attributes may include the purchaser's ability toobtain information for a given attribute (e.g., whether the purchasercan obtain information quickly and efficiently), addressable customerand article popularity, the impact of a given attribute on average unitcost, ability to validate values of each attribute, and/or ability tovalidate values of each attribute throughout a predetermined period oftime (e.g., a season). For each criterion, a value or description may beassigned for each attributes.

Table 1 shows an example of values assigned to various criteria withdifferent attributes. Table 1 may be used to determine which attributesmay be included in determining a quantity based on how much theseattributes impact the goals or targets set by the purchasers (e.g.,meeting cost and unit targets, or matching the quantity of one articlecategory to how popular such article category may be). In Table 1, “H”means the value is high, “M” means the value is in the middle, and “L”means the value is low. For example, for average unit cost, thepurchaser may be able to obtain information quickly and efficiently(high value), the addressable customer and article popularity may be low(low value), the impact of a given attribute on average unit cost may behigh (high value), the ability to validate values of each attribute maybe high (high value), and the ability to validate values of eachattribute throughout a season may be high (high value).

TABLE 1 Ability to Ease of Addressable Ability to validate obtainingaudience / Impact to validate (buy Attribute attribute popularity AUC(build) season) Average H L H H H Unit Cost Type of M H L M L CustomerLevel of M H L M L Conviction Article H M L H H Category

Step 202 may include obtaining, via the one or more processors,historical transactional data associated with purchasing the one or morearticles. The historical transactional data may include at least ahistorical size distribution associated with purchasing the one or morearticles. The historical size distribution may include any informationregarding sizes of one or more previously purchased articles (e.g.,article(s) purchased last year by a purchaser). The historicaltransactional data may include any suitable information regarding atransaction associated with purchasing or renting one or more articles,for example, a purchaser identifier, contact information (e.g., address,phone numbers, e-mail addresses, etc.), demographic information of thepurchaser or the customer (e.g., age, gender, marital status, incomelevel, educational background, number of children in household, etc.),purchaser preferences (preferences or reviews regarding favoriteproducts and/or services, favorite department stores/brands, etc.), atransaction amount, and previous transaction information. The previoustransaction information may include a time of a prior transaction,spending profile, past spending levels on goods, a frequency ofshopping, how much the purchaser spends in an average transaction, orhow much to spend on a particular product. The historical transactionaldata may be obtained via one or more employee devices 116, and may bestored in one or more databases associated with the entity providing theapparel subscription services (e.g., databases 106). The historicaltransactional data (e.g., a historical size distribution) may beadjusted or overridden based on changes that are anticipated (e.g., thedemand of sizes 10-16 may increase in 3 months). In some embodiments,the historical size distribution may be adjusted based on the one ormore assumptions described herein. The historical transactional data(e.g., historical demand of an article category) may be computed alongseveral different size scales based on production capacity of merchants(e.g., a merchant may produce sizes between 0 and 32, while others mayproduce sizes between 6 and 24). The historical transactional data mayinclude historical rental demand metrics. The historical rental demandmetrics may include any information regarding actual rental usage or aninterested/demand expressed by purchasers, customers, or users. In oneexample, a given article may be chosen by many customers or users of theapparel rental subscription services, but these customers or users maynot actually receive the given article (e.g., if demand for the articleexceeds supply). In this situation, a similar article, not the given(chosen) article, may be purchased, to meet the expressedinterest/demand in the future.

Step 203 may include obtaining, via the one or more processors, articlepreference data associated with purchasing the one or more articles. Thearticle preference data may include at least one of season impact dataor customer identification data. The season impact data may indicateseasonal impact on a trend or customer preference(s) for purchasing theone or more articles. For instance, during the winter season, outerwearand sweaters may be more preferable than T-shirt or short pants. Thecustomer identification data may include any information regarding acustomer's identity, including, for example, a customer name, a customeraddress, or a customer income range. The customer identification dataalso may include biometric data of the customer or any informationrelated to behavioral characteristics related to the pattern of behaviorof the user. The customer identification data may further include anyinformation pertaining to the customer, including, demographicinformation (e.g., age, gender, marital status, income level,educational background, number of children in household, etc.),employment, and other data related to the customer.

Step 204 may include determining, via the one or more processors, one ormore assumptions based on the article preference data. The one or moreassumptions may include one or more numerical values associated with theone or more article attributes. The one or more assumptions may beupdated periodically. For example, the one or more assumptions may beupdated or evaluated at the start of each season, based on the one ormore assumptions from the prior year (e.g., using one or moreassumptions used in spring of a first year for calculating one or moreassumptions of spring of the following year and a quantity and a sizedistribution of spring of the following year). One or more attributesand assumptions within the calculator may be evaluated each season, forexample. Over time, the one or more attributes that are used todetermine quantities by article category may be shifted, as businessstrategies and trends change. The one or more assumptions associatedwith the one or more attributes may change frequently, reflecting bothseasonal impact (e.g., an assumption associated with a dress in summermay be different from such assumption in winter). These changes of theone or more assumptions may be made at the outset of a new season,though, if desired, may be changed mid-season (e.g., to address toplinebusiness shifts). For example, if the anticipated number of customers ofthe apparel subscription services increases, the one or more assumptionsmay be adjusted and the quantity of the one or more articles mayincrease to ensure enough inventory for all the customers. The changesto one or more assumptions may be implemented in such a way thatpreviously-purchased styles with older assumptions, as well as remainingstyles to purchase with newer assumptions, may together satisfy anincrease in the total desired quantities for the season. Topline targetsmay include a purchasing plan (e.g., number of article categories topurchase, number of unique item identifier (e.g., stock keeping unit) topurchase, average quantity, average unit cost), as well as a targeteddistribution of article categories across various one or moreattributes. These targets may be established prior to purchasingarticles for an upcoming season and may be based on projected customernumber for the apparel rental subscription service(s), historicalpurchaser performance, and/or product performance. The topline targetsmay either determined by individuals (e.g., employees of an entityproviding the apparel rental subscription services) or by one or morealgorithms. The topline targets can be changed whenever required, e.g.,at the beginning of a season or mid-season.

Step 205 may include determining, via the one or more processors, thequantity associated with purchasing the one or more articles based onthe article information and the one or more assumptions. One or morealgorithms may be used to determine the quantity associated withpurchasing the one or more articles. For instance, one algorithm maycalculate the quantity associated with purchasing the one or morearticles based on a matrix (e.g., a matrix associated with average unitcost and a type of customer), the level of conviction, and the articlecategory. In one example, the quantity may be determined by multiplyingvalues assigned to the matrix, the level of conviction, and the articlecategory, respectively. The algorithm may work based on an indexingsystem. For example, one or more attributes may either be increased(index is greater than 1.0) or decreased (index is less than 1.0) fromthe average for each attribute selected by the purchaser.

FIG. 3 depicts an exemplary user interface presented on an employeedevice 116 for a purchaser employee 114 showing a quantity associatedwith purchasing one or more articles, according to one or moreembodiments of the present disclosure. In this example, the employeedevice 116 may be a laptop, desktop, mobile phone, tablet computer,etc., executing a display of, e.g., a website 300. The website 300 maybe displayed to the purchaser after a login status of the purchaser isdetermined and the purchaser is able to access the purchaser accountbased on the login status. In other embodiments, the informationillustrated in FIG. 3 may be presented in a different format viasoftware executed on an electronic device (e.g., a desktop, mobilephone, or tablet computer) serving as the employee device 116.

The website 300 may include one or more graphical elements. The one ormore graphical elements may include, but are not limited to, inputcontrols (e.g., checkboxes, radio buttons, dropdown lists, list boxes,buttons, toggles, text fields, date field), navigational components(e.g., breadcrumb, slider, search field, pagination, slider, tags,icons), informational components (e.g., tooltips, icons, progress bar,notifications, message boxes, modal windows), or containers (e.g.,accordion). As shown in FIG. 3, one or more graphical elements 302-312may enable a purchase to enter or select average unit cost 302, a typeof customer (e.g., audience) 304, a level of conviction 306, an articlecategory 308, and a size class 310 (e.g., an alphabetical or alpha sizeclass, or a numeric size class). The one or more graphical elements mayalso provide information regarding a quantity (e.g., depth) associatedwith purchasing one or more articles 312.

Regarding the average cost unit 302, after a purchaser types orotherwise specifies or selects the average unit cost 302, each articlecategory may automatically be categorized into different prices groups,such as the high price group, middle price group, or low price group. Inthe example illustrated in FIG. 3, all mid cost styles may be bought at83 units. In another example, all high cost styles may be bought at 27units. The dropdown list 304 may enable a purchaser to select whetherthe customer is a niche customer or a core customer, as describedherein. The dropdown list 306 may enable a purchaser to select a levelof conviction (e.g., high, middle, low). The level of conviction mayindicate, for example, an article category that the purchaser feelsstrongly to increase in quantity in the future. The dropdown list 308may enable a purchaser to select an article category. Once the quantityassociated with purchasing one or more articles is determined, thedropdown list 310 may enable a purchaser to select a size class in orderto view the quantity associated with purchasing one or more articles. Insome embodiments, purchasers may use predetermined values for a type ofcustomer and a level of conviction to ensure average quantity in linewith plan targets. One or more attributes may be changed slightlythroughout the season pending mid-season rollups. For articles belongingto minimum order quantity category and pre-pack category, the purchasermay not enter anything but accept a pre-determined quantity and sizedistribution, respectively. One or more assumptions 314 and relevantquantities 316 may be shown in the website 300.

Referring back to FIG. 2, step 206 may include determining, via the oneor more processors, the size distribution associated with purchasing theone or more articles based on the determined quantity, the historicaltransactional data, and the one or more assumptions. The method may beperformed in such a way that regardless of the determined quantity, thesize distribution may match exactly. In the event that the quantity iscalculated as a non-whole number (e.g., a decimal), one or moreoverrides may be used to ensure that the sum of the sizes recommended isnot more or less than the quantity recommended to purchase, which may beimportant for article category bought at lower quantity. The determinedquantity by size for the season may be in line with the demand byseason. The size distribution may indicate how the determined quantitydistributes among one or more sizes of an article category.

FIG. 4 depicts an exemplary user interface presented on an employeedevice for a purchaser showing a size distribution associated withpurchasing one or more articles, according to one or more embodiments ofthe present disclosure. In this example, the employee device 116 may bea laptop, desktop, mobile phone, tablet computer, etc. executing adisplay of, e.g., a website 400. The website 400 may display historicalaccount data to the purchaser after a login status of the purchaser isdetermined and the purchaser is able to access the purchaser accountbased on the login status. Additionally or alternatively, theinformation illustrated in FIG. 4 may be presented in a different formatvia software executing on an electronic device (e.g., a desktop, mobilephone, or tablet computer) serving as the employee device 116.

The website 400 may be shown to the purchaser after the quantityassociated with the purchasing one or more articles is determined. Inthe example shown in FIGS. 3 and 4, the quantity is 111. In the website400, four different results 402-408 of size distribution associated withpurchasing one or more articles may be shown. Different merchants mayproduce articles in different sizes. The four different results 402-408may represent four common sizes combinations. In some embodiments, theremay be other sizes combinations. The first result 402 may show the sizedistribution of 111 articles among sizes 0, 2, 4, 6, 8, 10, 12, 14, 16,14W, 16W, 18W, 20W, 22W, 24W, 26W, 28W, 30W, and 32W. The second result404 may show the size distribution of 111 articles among sizes 6, 8, 10,12, 14, 16, 14W, 16W, 18W, 20W, 22W, and 24W. The third result 406 mayshow the size distribution of 111 articles among sizes 4, 6, 8, 10, 12,14, 16, 14W, 16W, 18W, 20W, 22W, and 24W. The fourth result 408 may showthe size distribution of 111 articles among sizes 0, 2, 4, 6, 8, 10, 12,14, 16, 14W, 16W, 18W, 20W, 22W, 24W, and 26W.

Referring back to FIG. 4, step 207 may include transmitting, to apurchaser, a notification indicating the quantity and the sizedistribution associated with purchasing the one or more articles. Thenotification may include any information about the quantity and the sizedistribution associated with purchasing the one or more articles. Thenotification may be configured to be displayed on a display screen of auser device, an employee device, or a tenant device. The notificationmay be displayed on the display screen in any suitable form, such as ane-mail, a text message, a push notification, content on a web page,and/or any form of graphical user interface. The user device, employeedevice, or tenant device may be capable of accepting inputs of a user,an employee, or a tenant via one or more interactive components of theuser device, the employee device, or the tenant device, such as akeyboard, button, mouse, touchscreen, touchpad, joystick, trackball,camera, microphone, or motion sensor. The method may further include,prior to transmitting the notification to the purchaser, obtaining asize class associated with an article category. The size class may beobtained via one or more inputs from the purchaser (e.g., a purchaserselects a from a dropdown list associated with the size class). The sizeclass may include at least one of an alpha size class (e.g., XL, XLL) ora numerical size class (e.g., 0, 2, 4).

The method may further include providing one or more rules fordetermining the quantity and size distribution associated withpurchasing one or more articles. The one or more rules may include ifarticle belongs to minimum order quantity category or pre-pack category,referring to pre-determined quantity and size distribution; if thearticle category is in high price group, the article category may belabeled as low level of conviction (e.g., purchasing 27 unit); certainbrands of articles may have pre-determined or specifically-assignedvalues as these brands may deviate significantly from the preference ofcore customers and/or niche customers; articles that come in a differentsizes may still align with the total units based on the articlecategory, average unit cost, type of customer and level of conviction;or for certain article categories or brands (e.g., plus and missyarticles), average unit cost may be determined differently (e.g.,smaller units may be selected to determine the average unit cost).

FIG. 5 depicts an exemplary chart illustrating average unit cost (“AUC”)and quantity tolerance in an exemplary season. The exemplary season maybe a purchase period when one or more purchases purchase multiple timesduring the purchase period. The quantity tolerance may include anyinformation regarding the deviation of quantity or average unit costfrom the targets set by the purchaser. As shown in FIG. 5, for most ofthe style % into the plan, the average unit cost tolerance (“AUC VAR.ACT”) is higher than the quantity tolerance (“DEPTH VAR. ACT”). FIG. 6depicts an exemplary chart illustrating average unit cost purchased fordifferent price groups. As shown in FIG. 6, the average unit costchanges for different price groups. FIG. 5 and FIG. 6 may provideinformation to understand how average unit cost and determined quantityhas historically evolved during a period of time (e.g., a season). Sincepurchasers may purchase multiple times for a given season (e.g., apurchase period) over the length of the given season, and it may bedifficult to determine the time until the final article category ispurchased, it may be beneficial to have tolerances such that, with theremaining article categories left to purchase, the seasonal targets canstill be met. For example, article categories to be purchased at thebeginning of a purchase period may be more expensive than those at theend of the purchase period, so it may be beneficial to know what suchtrend typically looks like to develop tolerance and make reasonabledecisions. In one example, when 10% of article categories are purchased,the tolerance may include that the average unit costs may be 20% higherthan the targets set by the purchasers because the average unit costsmay decrease during the purchase period based on historical data. FIGS.5 and 6 provide examples of historical data may be useful during apurchase period to determine the tolerances based on how far into thetotal article category count the purchaser have bought.

At any stage of determining a quantity and a size distributionassociated with purchasing one or more articles, the method may furtherinclude storing data (e.g., article information, a quantity, a sizedistribution) for subsequent analysis. The stored data may have anexpiration period. The expiration period may be at least 1 day, 1 week,1 month, 1 quarter, 1 year or longer. In other embodiments, theexpiration period may be at most 1 year, 1 quarter, 1 month, 1 week, 1day or shorter. The subsequent analysis may include analyzing the datato update the quantity and size distribution associated with purchasingone or more articles, article information, historical transactionaldata, or article preference data.

A machine learning model may be used to determine a quantity and a sizedistribution associated with purchasing one or more articles. Themachine learning model may be a regression-based model or classificationmodel that accepts the data identified in any steps described above asinput data. Regression models may predict a number (e.g., a quantity of111). The machine learning model may be of any suitable form, and mayinclude, for example, a neural network, linear regression, logisticregression, support vector machines (SVM), naïve Bayes classifiers, ormay include tree-based methods such as random forest or gradientboosting machines (GBM). A neural network may be software representinghuman neural system (e.g., cognitive system). A neural network mayinclude a series of layers termed “neurons” or “nodes.” A neural networkmay include an input layer, to which data is presented; one or moreinternal layers; and an output layer. The number of neurons in eachlayer may be related to the complexity of a problem to be solved. Inputneurons may receive data being presented and then transmit the data tothe first internal layer through connections' weight. A neural networkmay include a convolutional neural network, a deep neural network, or arecurrent neural network.

The machine learning model may produce the quantity and a sizedistribution associated with purchasing one or more articles as afunction of the article information, the historical transactional data,the article preference data, the one or more assumptions, or one or morevariables indicated in the input data. The one or more variables may bederived from the article information, the historical transactional data,the article preference data, or the one or more assumptions. Thisfunction may be learned by training the machine learning model withtraining sets. The machine learning model may be trained by supervised,unsupervised, or semi-supervised learning using training sets comprisingdata of types similar to the type of data used as the model input. Forexample, the training set used to train the model may include anycombination of the following: the article information, the historicaltransactional data, the article preference data, or the one or moreassumptions. Accordingly, the machine learning model may be trained tomap input variables to a quantity or value of the quantity and a sizedistribution associated with purchasing one or more articles. Thequantity and a size distribution associated with purchasing one or morearticles determined by the machine learning model may be used as anadditional input variable.

In general, any process discussed in this disclosure that is understoodto be computer-implementable, such as the processes illustrated in FIG.2, may be performed by one or more processors of a computer system or aserver system 102, as described above. A process or process stepperformed by one or more processors may also be referred to as anoperation. The one or more processors may be configured to perform suchprocesses by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable types of processing unit.

A computer system, such as a server system 112, may include one or morecomputing devices. If the one or more processors of the server system112 are implemented as a plurality of processors, the plurality ofprocessors may be included in a single computing device or distributedamong a plurality of computing devices. If a server system 112 includesa plurality of computing devices, the memory of the server system 112may include the respective memory of each computing device of theplurality of computing devices.

FIG. 7 illustrates an example of a computing device 700 of a computersystem. The computing device 700 may include processor(s) 710 (e.g.,CPU, GPU, or other such processing unit(s)), a memory 720, andcommunication interface(s) 740 (e.g., a network interface) tocommunicate with other devices. Memory 720 may include volatile memory,such as RAM, and/or non-volatile memory, such as ROM and storage media.Examples of storage media include solid-state storage media (e.g., solidstate drives and/or removable flash memory), optical storage media(e.g., optical discs), and/or magnetic storage media (e.g., hard diskdrives). The aforementioned instructions (e.g., software orcomputer-readable code) may be stored in any volatile and/ornon-volatile memory component of memory 720. The computing device 700may, in some embodiments, further include input device(s) 750 (e.g., akeyboard, mouse, or touchscreen) and output device(s) 760 (e.g., adisplay, printer). The aforementioned elements of the computing device700 may be connected to one another through a bus 730, which representsone or more busses. In some embodiments, the processor(s) 710 of thecomputing device 700 includes both a CPU and a GPU.

Instructions executable by one or more processors may be stored on anon-transitory computer-readable medium. Therefore, whenever acomputer-implemented method is described in this disclosure, thisdisclosure shall also be understood as describing a non-transitorycomputer-readable medium storing instructions that, when executed by oneor more processors, cause the one or more processors to perform thecomputer-implemented method. Examples of non-transitorycomputer-readable medium include RAM, ROM, solid-state storage media(e.g., solid state drives), optical storage media (e.g., optical discs),and magnetic storage media (e.g., hard disk drives). A non-transitorycomputer-readable medium may be part of the memory of a computer systemor separate from any computer system.

It should be appreciated that in the above description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various inventive aspects. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaims require more features than are expressly recited in each claim.Rather, as the following claims reflect, inventive aspects lie in lessthan all features of a single foregoing disclosed embodiment. Thus, theclaims following the Detailed Description are hereby expresslyincorporated into this Detailed Description, with each claim standing onits own as a separate embodiment of this disclosure.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe disclosure, and form different embodiments, as would be understoodby those skilled in the art. For example, in the following claims, anyof the claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the disclosure, and it isintended to claim all such changes and modifications as falling withinthe scope of the disclosure. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be added or deleted to methods describedwithin the scope of the present disclosure.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted.

1-20. (canceled)
 21. A computer-implemented method for determining arecommended quantity and a size distribution associated with one or morearticles, the method comprising: training a machine learning model tomap input variables to the recommended quantity associated with the oneor more articles, the recommended quantity indicating a number ofarticles to be made available through an electronic platform; executingthe machine learning model to determine the recommended quantityassociated with the one or more articles, the determining based onarticle information and one or more assumptions of demand; executing themachine learning model to determine the size distribution associatedwith the one or more articles based on the recommended quantity,historical transactional data, and the one or more assumptions ofdemand; subsequently training the machine learning model using the sizedistribution associated with the one or more articles based on therecommended quantity, the historical transactional data, and the one ormore assumptions of demand; and in accordance with the subsequentlytraining, displaying a notification indicating the recommended quantityand the size distribution associated with the one or more articles. 22.The computer-implemented method of claim 21, wherein the machinelearning model is trained with at least one training set to determinethe recommended quantity.
 23. The computer-implemented method of claim21, wherein the machine learning model is trained with at least onetraining set to determine the size distribution.
 24. Thecomputer-implemented method of claim 21, wherein the input variablescomprises one or more attributes, the one or more attributes including alikelihood prediction of one or more service users requesting the one ormore articles of an article category.
 25. The computer-implementedmethod of claim 21, wherein the historical transactional data includesprevious transaction data, at least one historical rental demand metric,and a historical size distribution associated with the one or morearticles.
 26. The computer-implemented method of claim 21, wherein thearticle information comprises whether the one or more articles belong toa minimum order quantity category.
 27. The computer-implemented methodof claim 21, wherein the article information comprises whether the oneor more articles belong to a pre-pack category.
 28. Thecomputer-implemented method of claim 21, wherein the one or moreassumptions of demand include one or more numerical values associatedwith one or more article attributes.
 29. The computer-implemented methodof claim 21, wherein the one or more assumptions of demand are updatedperiodically.
 30. The computer-implemented method of claim 21, whereinthe one or more assumptions of demand are based on article preferencedata.
 31. The computer-implemented method of claim 31, wherein thearticle preference data comprises season impact data that indicatesseasonal impact on a trend or service user preference for the one ormore articles.
 32. A computer system for determining a recommendedquantity and a size distribution associated with one or more articles,comprising: a memory storing instructions; and one or more processorsconfigured to execute the instructions to perform operations including:training a machine learning model to map input variables to therecommended quantity associated with the one or more articles, therecommended quantity indicating a number of articles to be madeavailable through an electronic platform; executing the machine learningmodel to determine the recommended quantity associated with the one ormore articles, the determining based on article information and one ormore assumptions of demand; executing the machine learning model todetermine the size distribution associated with the one or more articlesbased on the recommended quantity, historical transactional data, andthe one or more assumptions of demand; subsequently training the machinelearning model using the size distribution associated with the one ormore articles based on the recommended quantity, the historicaltransactional data, and the one or more assumptions of demand; and inaccordance with the subsequently training, displaying a notificationindicating the recommended quantity and the size distribution associatedwith the one or more articles.
 33. The computer system of claim 32,wherein the machine learning model is trained with at least one trainingset to determine the recommended quantity.
 34. The computer system ofclaim 32, wherein the machine learning model is trained with at leastone training set to determine the size distribution.
 35. The computersystem of claim 32, wherein the input variables comprises one or moreattributes, the one or more attributes including a likelihood predictionof one or more service users requesting the one or more articles of anarticle category.
 36. The computer system of claim 32, wherein the sizedistribution indicates how the determined quantity distributes among oneor more sizes of an article category.
 37. A non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofdetermining a recommended quantity and a size distribution associatedwith one or more articles, the method comprising: training a machinelearning model to map input variables to the recommended quantityassociated with the one or more articles, the recommended quantityindicating a number of articles to be made available through anelectronic platform; executing the machine learning model to determinethe recommended quantity associated with the one or more articles, thedetermining based on article information and one or more assumptions ofdemand; executing the machine learning model to determine the sizedistribution associated with the one or more articles based on therecommended quantity, historical transactional data, and the one or moreassumptions of demand; subsequently training the machine learning modelusing the size distribution associated with the one or more articlesbased on the recommended quantity, the historical transactional data,and the one or more assumptions of demand; and in accordance with thesubsequently training, displaying a notification indicating therecommended quantity and the size distribution associated with the oneor more articles.
 38. The non-transitory computer readable medium ofclaim 37, wherein the machine learning model is trained with at leastone training set to determine the recommended quantity.
 39. Thenon-transitory computer readable medium of claim 37, wherein the machinelearning model is trained with at least one training set to determinethe size distribution.
 40. The non-transitory computer readable mediumof claim 37, wherein the input variables comprises one or moreattributes, the one or more attributes including a likelihood predictionof one or more service users requesting the one or more articles of anarticle category.